An Image-based model for 3D shape quality measure

Alhamazani, Fahd, Rosin, Paul ORCID: https://orcid.org/0000-0002-4965-3884 and Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 2023. An Image-based model for 3D shape quality measure. Presented at: EG UK Computer Graphics & Visual Computing, Aberystwyth, UK, 14-15 September 2023. Proceedings EG UK Computer Graphics & Visual Computing. Eurographics - The European Association for Computer Graphics, 10.2312/cgvc.20231187

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Abstract

In light of increased research on 3D shapes and the increased processing capability of GPUs, there has been a significant increase in available 3D applications. In many applications, assessment of perceptual quality of 3D shapes is required. Due to the nature of 3D representation, this quality assessment may take various forms. While it is straightforward to measure geometric distortions directly on the 3D shape geometry, such measures are often inconsistent with human perception of quality. In most cases, human viewers tend to perceive 3D shapes from their 2D renderings. It is therefore plausible to measure shape quality using their 2D renderings. In this paper, we present an image-based quality metric for evaluating 3D shape quality given the original and distorted shapes. To provide a good coverage of 3D geometry from different views, we render each shape from 12 equally spaced views, along with a variety of rendering styles to capture different aspects of visual characteristics. Image-based metrics such as SSIM (Structure Similarity Index Measure) are then used to measure the quality of 3D shapes. Our experiments show that by effectively selecting a suitable combination of rendering styles and building a neural network based model, we achieve significantly better prediction for subjective perceptual quality than existing methods.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Publisher:	Eurographics - The European Association for Computer Graphics
Date of First Compliant Deposit:	20 September 2023
Date of Acceptance:	25 July 2023
Last Modified:	11 Jun 2024 12:36
URI:	https://orca.cardiff.ac.uk/id/eprint/162646

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